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A dynamic neural field model of vowel diphthongisation

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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A dynamic neural field model of vowel diphthongisation. / Kirkham, Sam; Strycharczuk, Patrycja.
Proceedings of the 13th International Seminar on Speech Production. ed. / Cécile Fougeron; Pascal Perrier. International Speech Communication Association, 2024. p. 205-208.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Kirkham, S & Strycharczuk, P 2024, A dynamic neural field model of vowel diphthongisation. in C Fougeron & P Perrier (eds), Proceedings of the 13th International Seminar on Speech Production. International Speech Communication Association, pp. 205-208, ISSP 2024 : 13th International Seminar on Speech Production, Autrans, France, 13/05/24. https://doi.org/10.21437/issp.2024-52

APA

Kirkham, S., & Strycharczuk, P. (2024). A dynamic neural field model of vowel diphthongisation. In C. Fougeron, & P. Perrier (Eds.), Proceedings of the 13th International Seminar on Speech Production (pp. 205-208). International Speech Communication Association. https://doi.org/10.21437/issp.2024-52

Vancouver

Kirkham S, Strycharczuk P. A dynamic neural field model of vowel diphthongisation. In Fougeron C, Perrier P, editors, Proceedings of the 13th International Seminar on Speech Production. International Speech Communication Association. 2024. p. 205-208 doi: 10.21437/issp.2024-52

Author

Kirkham, Sam ; Strycharczuk, Patrycja. / A dynamic neural field model of vowel diphthongisation. Proceedings of the 13th International Seminar on Speech Production. editor / Cécile Fougeron ; Pascal Perrier. International Speech Communication Association, 2024. pp. 205-208

Bibtex

@inproceedings{86d6228e2a2e48218887f2d48ab9fa5f,
title = "A dynamic neural field model of vowel diphthongisation",
abstract = "We advance a computational model of vowel diphthongisation that situates phonological representations in dynamic neural fields (DNFs), which represent the time-varying activation of neural populations that are sensitive to a given phonetic parameter range. We model all long vowels as two separate inputs to the DNF, with input timing governed by a coupled oscillator model that generates an anti-phase relationship between inputs. The location of time-varying maximum activation in the DNF forms a noisy dynamic target, which is used as input to a task dynamic model of gestural coordination. We find that spatial characteristics of long vowels are well captured by the model, which exhibits gradient variation between monophthongs and diphthongs. We also show that a simplified model of production/perception can simulate changes in a speaker{\textquoteright}s phonological planning representations, which could represent a mechnnism behind sound change if transmitted across a community.",
author = "Sam Kirkham and Patrycja Strycharczuk",
year = "2024",
month = may,
day = "13",
doi = "10.21437/issp.2024-52",
language = "English",
pages = "205--208",
editor = "C{\'e}cile Fougeron and Pascal Perrier",
booktitle = "Proceedings of the 13th International Seminar on Speech Production",
publisher = " International Speech Communication Association",
note = "ISSP 2024 : 13th International Seminar on Speech Production ; Conference date: 13-05-2024 Through 17-05-2024",
url = "https://issp24.inviteo.fr/",

}

RIS

TY - GEN

T1 - A dynamic neural field model of vowel diphthongisation

AU - Kirkham, Sam

AU - Strycharczuk, Patrycja

N1 - Conference code: 13th

PY - 2024/5/13

Y1 - 2024/5/13

N2 - We advance a computational model of vowel diphthongisation that situates phonological representations in dynamic neural fields (DNFs), which represent the time-varying activation of neural populations that are sensitive to a given phonetic parameter range. We model all long vowels as two separate inputs to the DNF, with input timing governed by a coupled oscillator model that generates an anti-phase relationship between inputs. The location of time-varying maximum activation in the DNF forms a noisy dynamic target, which is used as input to a task dynamic model of gestural coordination. We find that spatial characteristics of long vowels are well captured by the model, which exhibits gradient variation between monophthongs and diphthongs. We also show that a simplified model of production/perception can simulate changes in a speaker’s phonological planning representations, which could represent a mechnnism behind sound change if transmitted across a community.

AB - We advance a computational model of vowel diphthongisation that situates phonological representations in dynamic neural fields (DNFs), which represent the time-varying activation of neural populations that are sensitive to a given phonetic parameter range. We model all long vowels as two separate inputs to the DNF, with input timing governed by a coupled oscillator model that generates an anti-phase relationship between inputs. The location of time-varying maximum activation in the DNF forms a noisy dynamic target, which is used as input to a task dynamic model of gestural coordination. We find that spatial characteristics of long vowels are well captured by the model, which exhibits gradient variation between monophthongs and diphthongs. We also show that a simplified model of production/perception can simulate changes in a speaker’s phonological planning representations, which could represent a mechnnism behind sound change if transmitted across a community.

U2 - 10.21437/issp.2024-52

DO - 10.21437/issp.2024-52

M3 - Conference contribution/Paper

SP - 205

EP - 208

BT - Proceedings of the 13th International Seminar on Speech Production

A2 - Fougeron, Cécile

A2 - Perrier, Pascal

PB - International Speech Communication Association

T2 - ISSP 2024 : 13th International Seminar on Speech Production

Y2 - 13 May 2024 through 17 May 2024

ER -